a1260cc19342c4db61c6942a11c2b2b2b58f8bad,python/tvm/relay/frontend/keras.py,,_convert_reshape,#Any#Any#Any#,863

Before Change

    if len(inshape) == 3 and len(tshape) == 1:
        // (?, a, b) -> (-1, ab)
        shape = (-1, tshape[0])
    elif len(inshape) in [2, 3] and len(tshape) == 2:
        // (?, cc) -> (-1, c, c)
        // (?, a, b) -> (-1, c, c)
        assert tshape[0] == tshape[1], "Only supports square target shapes, but got {}".format(
            tshape
        )
        shape = (-1,) + tshape
    else:
        // (?, h, w, c) -> (-1, c, H, W)
        // (?, h, w, c) -> (-1, c, hw)
        // (?, hw, c) -> (-1, c, h, w)
        ch = inshape[-1]
        assert ch == tshape[-1], (
            "Only supports last dimension in target shape being equal to "
            "the channel number of input tensor."
        )
        if etab.data_layout == "NCHW":
            shape = (-1, ch) + tshape[:-1]
        else:
            shape = (-1,) + tshape[:-1] + (ch,)
    return _op.reshape(inexpr, newshape=shape)


def _convert_lstm(inexpr, keras_layer, etab):

After Change

    if etab.data_layout == "NCHW" and (len(inshape) > 3 or len(tshape) > 2):
        // Perform reshape in original NHWC format.
        inexpr = _op.transpose(inexpr, [0] + list(range(2, len(inshape))) + [1])
        inexpr = _op.reshape(inexpr, newshape=shape)
        return _op.transpose(inexpr, axes=[0, -1] + list(range(1, len(shape) - 1)))

    return _op.reshape(inexpr, newshape=shape)

Instances